Age-Group Differences in Human Papillomavirus Types and Cofactors for Cervical Intraepithelial Neoplasia 3 among Women Referred to Colposcopy
Bibliographic record
Abstract
BACKGROUND: Recommendations for high-risk human papillomavirus (HR-HPV) testing as an adjunct to cytology for cervical cancer screening differ by age group, because HR-HPV tests lack adequate specificity in women aged <30. Here, we assess age-group differences in HPV types and other risk factors for cervical intraepithelial neoplasia (CIN) grade 3 or worse (CIN3+) versus CIN0-2 in women from four colposcopy clinics. METHODS: Women ages 18 to 69 (n = 1,658) were enrolled and completed structured interviews to elicit data on behavioral risk factors prior to their examinations. HPV genotyping was done on exfoliated cervical cell samples. We estimated relative risks (RR) for HPV types and cofactors for CIN3+, overall and stratified by age group. RESULTS: After 2 years of follow-up, we identified 178 CIN3+, 1,305 CIN0-2, and 175 indeterminate outcomes. Nonvaccine HR-HPV types were only associated with CIN3+ among women ≥ 30 (RR = 2.3, 95% CI: 1.5-3.4; <30: RR = 0.9). Among all HR-HPV-positive women, adjusting for age, significant cofactors for CIN3+ included current smoking (RR = 1.5), former smoking (RR = 1.8), regular Pap screening (RR = 0.7), current regular condom use (RR = 0.5), and parity ≥ 5 (RR = 1.6, P(trend) for increasing parity = 0.07). However, the parity association differed by age group (≥ 30: RR = 1.8, P(trend) = 0.008; <30: RR = 0.9; P(trend) =.55). CONCLUSION: Subgroup variation by age in the risk of CIN3+ points to the importance of the timing of exposures in relation to CIN3+ detection. IMPACT: Future screening strategies need to consider natural history and secular trends in cofactor prevalence in the pursuit of appropriately sensitive and specific screening tools applied to appropriate age groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".